Academic Researchers in Social Sciences
For social science researchers, understanding complex causal relationships is crucial. Causal Map provides a robust methodology for analysing qualitative data, allowing you to uncover intricate causal networks in your research.
Why Causal Map?
- Analyse large volumes of qualitative data from interviews, focus groups, and literature reviews
- Identify emergent themes and causal relationships and identify unexpected outcomes
- Visualise complex social systems and phenomena
- Support mixed-methods research designs
- Easily verify the evidence for causal links
Case Study: The Dynamics of the UN Voluntary Local Review using Causal Mapping within and across the Sustainable Development Goals: a case study of Bath and North East Somerset
In this publication, Causal Map was used to analyse the dynamics of the UN Voluntary Local Review (VLR) process across 20 cities implementing the Sustainable Development Goals (SDGs). The research team used Causal Map to process and visualise data from 20 VLR reports, creating both individual city maps and an aggregate map of 805 causal links. Causal Map generated clear visualizations of the complex relationships between various factors influencing SDG implementation, facilitating easier interpretation and communication of findings and decision making.
Case Study: Evaluation project about gender gaps faced by women pursuing STEM careers at DuocUC
DuocUC, a higher education institution in Chile, hired our consultancy to conduct QuIP-style interviews with Qualia and analyse them using the Causal Map app. The interviews were motivated by concerns about the gender gaps faced by women pursuing STEM careers at the university.
We set up the interviews in Qualia to ask questions about changes in 3 domains: educational experiences, professional development and relationship dynamics. The instructions for the AI interviewer were similar to the instructions you could give to a human interviewer. Both the interview instructions and the interviews itself were conducted in Spanish.
We collected stories from 32 DuocUC students and alumni and uploaded the interview transcripts into Causal Map to analyse the causal relationships mentioned by the respondents. We used AI (GPT-4o) to identify each and every causal link in the interviews, and for each link, to label the cause and effect. We used a “radical zero-shot” approach in which the AI is given no codebook and is simply told to invent its own codes (in Spanish). We gave the AI context about the project.
Once the coding was done, we used the filters in the app to create different maps that answered their research questions:
- What was the immediate impact on the respondents’ lives because of gender discrimination?
- What is the causal network starting from gender discrimination?
- What are the most frequently mentioned factors?
Check our subscription options if you’re interested in using the app yourself or our consultancy packages if you prefer that we do the work for you!